Improving a Multi-Reference GPS Station Network Method for OTF Positioning in the St. Lawrence Seaway
Bibliographic record
Abstract
Real time kinematic GPS positioning is able to provide cm-level positioning accuracies, as long as the carrier phase ambiguities are resolved on-the-fly (OTF) to integer values. Classical methods are based on differential positioning using a single fixed reference station located in the vicinity of the rover. The maximum distance allowed between the reference station and user is generally limited by the effects of the atmosphere and orbit. A novel and unique method was developed at the University of Calgary, which uses all available reference stations to optimally generate regional code and carrier phase corrections, which can be transmitted to the user in order to resolve integer ambiguities OTF over the region. One of the major advantages of this method is to increase the coverage under which successful OTF ambiguity resolution is possible. This method has been tested using several data sets collected under various atmospheric conditions in the world. The improvement brought by the method was very good in practically all cases. Further research has been developed at the University of Calgary towards optimizing the method in order to maximize the improvement obtained by it. This new approach, also using least square collocation, separately models the errors into ionospheric, tropospheric and satellite orbital components. An additional effort has been carried out in terms of modeling the ionosphere into directional components, which has shown to be relevant under high ionospheric conditions. Results of the enhanced method are presented using data collected in the St. Lawrence Seaway region, Canada, and compared with the ones obtained modeling the total error in L1 and Wide Lane carrier phases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".